QAT(quantize aware training) for classification with MQBench
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Updated
Nov 18, 2021 - Python
QAT(quantize aware training) for classification with MQBench
This is a project documentation about melanoma detection methods using convolutional neural networks.
🔪 Elimination based Lightweight Neural Net with Pretrained Weights
American Sign Language Alphabet Detection in Real Time using OpenCV-Mediapipe with EfficientNetB0 in PyTorch
An implementation of the Arabic sign language classification using Keras on the zArASL_Database_54K dataset
Development of a depth estimation model based on a UNET architecture - connection of Bi-directional Feature Pyramid Network (BIFPN) and EfficientNet.
The purpose of Food Vision project is to classify 101 variety of food items using Machine Learning.
SkinNet Analyzer: A Deep Learning-Based Skin Disease Detection System - College Final Year (4th year) Project
HAM10000 Skin Lesion Classification
Benchmarking CNNs and Vision Transformers on CIFAR-10/100 using a unified PyTorch pipeline with transfer learning and model fusion.
CoalClassifier: A deep learning model for classifying coal types using EfficientNetB0-based transfer learning and fine-tuning techniques. This project is designed to accurately distinguish between Anthracite, Bituminous, Lignite, and Peat classes and is developed using TensorFlow/Keras
A multi classification using scikit-learn and TensorFlow models on MRI scans of patient's brains.
Image Captioning using EfficientNet and GRU
Dust detection on solar photovoltaics panel using pre-trained CNN models
49.5 mAP50 Detector enet4y2-coco.cfg = EfficientnetB0 + 4YOLO Layers + BiDirectionalFeatureMap with COCO Dataset and 81.0 mAP50 with VOC2007 test Dataset.
A Deep Learning application for Malaria Detection
A state-of-the-art, open-source deepfake detection system built with PyTorch and EfficientNet-B0, featuring a user-friendly web interface for real-time image and video analysis.
Deepfake detection leveraging the OpenForensics dataset, a comprehensive resource for face forgery detection and segmentation research. The project explores various deep learning models and evaluates their performance in distinguishing between real and fake images of human faces.
Brain tumor MRI classification using deep learning models such as ResNet18 and EfficientNet-B0 to identify the most effective architecture for accurate tumor detection.
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